Publications

Journal Papers

• Lavagna, L., Carillo, S., & Panella, M.
  A topical review on time-independent perturbation theory in one-dimensional quantum systems.
  Physica Scripta, 100 (10), 1–33, 2025.
  https://doi.org/10.1088/1402-4896/ae0a8f
  Keywords: one-dimensional quantum systems; perturbation theory; quantum computing.

• Lavagna, L., Piperno, S., Ceschini, A., & Panella, M.
  Small graph perturbations, QAOA, and the MaxCut problem.
  AVS Quantum Science, 1–14, 2025.
  Publisher: AIP Publishing; AVS Science and Technology of Materials, Interfaces and Processing.
  ISSN: 2639-0213.
  Keywords: quantum approximate optimization algorithm; MaxCut problem; graph perturbations; quantum computing.

• Buttitta, G., Lavagna, L., Bonacorsi, S., Barbarito, C., Moliterno, M., Saito, G., Oddone, I., Verdone, G., Raimondi, S., & Panella, M.
  Machine Learning-Guided microfluidic optimization of clinically inspired liposomes for nanomedicine applications.
  International Journal of Pharmaceutics, 686, 126362, 2025.
  https://doi.org/10.1016/j.ijpharm.2025.126362
  Keywords: machine learning; artificial intelligence; liposomes; microfluidics; nanomaterial; nanomedicine.

Conference Papers

• Lavagna, L., Ceschini, A., Rosato, A., & Panella, M.
  Novel Quantum Approaches to Hyperdimensional Computing for Neural Networks.
  In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2025), Rome, Italy, IEEE, pp. 1–8, 2025.
  https://doi.org/10.1109/IJCNN64981.2025.11229083
  Keywords: Hands; quantum computing; accuracy; computational modeling; neural networks; machine learning; computer architecture; computational efficiency; quantum circuit; testing.

• Casalbore, M., Lavagna, L., Rosato, A., & Panella, M.
  Hybrid Quantum-Classical Framework for Anomaly Detection in Time Series with QUBO formulation and QAOA.
  In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2025), Rome, Italy, IEEE, pp. 1–8, 2025.
  https://doi.org/10.1109/IJCNN64981.2025.11228152
  Keywords: statistical analysis; time series analysis; pipelines; qubit; noise; computer architecture; quantum circuit; anomaly detection; optimization; tuning.

• De Falco, F., Lavagna, L., Ceschini, A., Rosato, A., & Panella, M.
  Evolving hybrid quantum-classical GRU architectures for multivariate time series.
  In IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2024), IEEE Computer Society, pp. 1–6, 2024.
  https://doi.org/10.1109/MLSP58920.2024.10734792
  Keywords: multivariate time series; quantum computing; quantum gated recurrent units; quantum machine learning.

• Lavagna, L., Ceschini, A., Rosato, A., & Panella, M.
  A layerwise-multi-angle approach to fine-tuning the quantum approximate optimization algorithm.
  In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2024), IEEE, pp. 1–6, 2024.
  https://doi.org/10.1109/IJCNN60899.2024.10650075
  Keywords: quantum approximate optimization algorithm; layerwise-multi-angle approach; quantum computing.

• Lavagna, L., De Falco, F., Ceschini, A., Rosato, A., & Panella, M.
  Trade-offs in Cryptosystems by Boolean and Quantum Circuits.
  In Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS 2025), IEEE, pp. 1–5, 2025.
  https://doi.org/10.1109/ISCAS56072.2025.11043205
  Keywords: fault tolerance; circuits and systems; encryption; quantum mechanics; circuit theory.

Other

• Lavagna, L., De Falco, F., & Panella, M.
  Quantum Hyperdimensional Computing for Pattern Completion.
  In Proceedings of Quantum Techniques in Machine Learning (QTML 2025), pp. 1–1, 2025.

• Lavagna, L., De Falco, F., & Panella, M.
  The Effectiveness of Classical and Hybrid Models for MaxCut problem.
  In Proceedings of Quantum Techniques in Machine Learning (QTML 2025), pp. 1–1, 2025.

• Lavagna, L., De Falco, F., & Panella, M.
  Is the QAOA the Ultimate Solution for the MaxCut problem?
  In Proceedings of Quantum Techniques in Machine Learning (QTML 2025), pp. 1–1, 2025.

• Ceschini, A., Lavagna, L., De Falco, F., Rosato, A., & Panella, M.
  Circuiti neurali quantistici per il processamento di grafi, immagini e serie temporali.
  In Memorie ET2025, Gruppo Nazionale Ricercatori di Elettrotecnica, Italia, pp. 1–2, 2025.

• Lavagna, L., Ceschini, A., Piperno, S., Casalbore, M., Rosato, A., & Panella, M.
  Soluzioni quantistico-classiche per ottimizzazione e rilevamento di anomalie.
  In Memorie ET2025, Gruppo Nazionale Ricercatori di Elettrotecnica, Italia, pp. 1–2, 2025.

• Rosato, A., Lavagna, L., & Panella, M.
  Integrazione del calcolo iperdimensionale nei circuiti digitali, nelle reti neurali e nelle architetture computazionali quantistiche.
  In Memorie ET2025, Gruppo Nazionale Ricercatori di Elettrotecnica, Italia, pp. 1–2, 2025.

• Ceschini, A., Lavagna, L., De Falco, F., Rosato, A., & Panella, M.
  Convergenza e generalizzazione nelle reti neurali quantistiche.
  In Memorie ET2024, Gruppo Nazionale Ricercatori di Elettrotecnica, Italia, pp. 1–2, 2024.

• De Falco, F., Piperno, S., Lavagna, L., Ceschini, A., Rosato, A., & Panella, M.
  Enhancing QAOA Ansatz via Multi-Parameterized Layer and Blockwise Optimization.
  In Proceedings of Quantum Techniques in Machine Learning (QTML 2024), pp. 1–3, 2024.

• Lavagna, L., De Falco, F., Piperno, S., Ceschini, A., Rosato, A., & Panella, M.
  Quantum Generative Modeling via Straightforward State Preparation.
  In Proceedings of Quantum Techniques in Machine Learning (QTML 2024), University of Melbourne, Melbourne, Australia, pp. 1–1, 2024.

• Piperno, S., Lavagna, L., De Falco, F., Ceschini, A., Rosato, A., Windridge, D., & Panella, M.
  Quantum Enhanced Knowledge Distillation.
  In Proceedings of Quantum Techniques in Machine Learning (QTML 2024), University of Melbourne, Melbourne, Australia, pp. 1–1, 2024.